Welcome to standalone version of PRR-HyPred


PRR-HyPred is a two-layer ML-based hybrid easy to use webserver for the prediction of pattern recognition receptors (PRRs). PRR-HyPred was developed by using different feature sets that includes, amino acid composition, dipeptide composition, physiochemical properties, and their hybrids as an input feature. The PRR-HyPred working module is based on optimally selected hybrid features in which the first layer predicts whether a given sequence is a PRR or non-PRR, and the second layer assigs specific family to the predicted PRR sequence. In addition to the prediction results, the probability scores for each prediction are also provided.


To use PRR-HyPred as a standalone tool click Download
After download is complete, uncompress the file "PRR-HyPred_standalone.tar.gz", e.g.
On Linux: tar -xvzf PRR-HyPred_standalone.tar.gz
On Windows: To uncompress files with .tar.gz extension on Windows Click Here
Uncompressing the file will create a directory named "PRR-HyPred_standalone"
Go to this directory and follow the instructions provided in the readme file
Positive Dataset: Download
Negative Dataset: Download
Family-specific Dataset: Download


Reference:
PRR-HyPred: A machine learning-based two-layer hybrid framework to predict pattern-recognition receptors and their families by employing sequence encoded optimal features (Manuscript Submitted): DOI:xxxx
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